Social service professionals operate in legally sensitive, administratively intensive, and context-dependent environments in which decision-making requires the simultaneous interpretation of regulatory norms, institutional procedures, and individual case circumstances. This paper proposes a conceptual model of a Personal Legal and Social Artificial Intelligence (AI) Assistant intended to support professional decision-making in social services, and demonstrates its functionality through a working prototype. The model is formulated as a domain-specific retrieval-augmented generation (RAG) framework in which a controlled legal and social document corpus is processed through text extraction, chunking, semantic indexing via SentenceTransformer embeddings, top-k retrieval through cosine similarity, and bounded large-language-model reasoning to produce grounded and explainable responses. The proposed framework is informed by three successive prototype versions and by observed sensitivity to corpus scope, document prioritization, and prompt constraints. The current prototype version operates on a prioritized corpus of sixteen Bulgarian normative acts complemented by three supplementary resources, comprising 883 indexed fragments, and uses DeepSeek as the reasoning model accessed through the OpenRouter API. The functionality of the model is validated through a representative use case concerning child protection, in which the prototype identifies the applicable legal provisions, exposes the retrieved documentary evidence, and generates a four-part structured analysis comprising legal qualification, applicable provisions, legal consequences, and recommendations for action. The main contribution lies in the formalization and prototype-level demonstration of a domain-specific AI assistant that combines legal grounding, social-context awareness, and bounded language-model reasoning for trustworthy decision support in regulated social-service practice.